Hiroshi Koshimizu1, Ryosuke Kojima2, Kazuomi Kario3, Yasushi Okuno4. 1. Department of Biomedical Data Intelligence, Graduate School of Medicine, Kyoto University, Kyoto, 606-8507, Japan; Development Center, Omron Healthcare Co., Ltd., Kyoto, 617-0002, Japan. Electronic address: koshimizu.hiroshi.87a@st.kyoto-u.ac.jp. 2. Department of Biomedical Data Intelligence, Graduate School of Medicine, Kyoto University, Kyoto, 606-8507, Japan. 3. Division of Cardiovascular Medicine, Department of Medicine, School of Medicine, Jichi Medical University, Tochigi, 329-0431, Japan. 4. Department of Biomedical Data Intelligence, Graduate School of Medicine, Kyoto University, Kyoto, 606-8507, Japan. Electronic address: okuno.yasushi.4c@kyoto-u.ac.jp.
Abstract
PURPOSE: The purpose of our study was to predict blood pressure variability from time-series data of blood pressure measured at home and data obtained through medical examination at a hospital. Previous studies have reported the blood pressure variability is a significant independent risk factor for cardiovascular disease. METHODS: We adopted standard deviation for a certain period and predicted variabilities and mean values of blood pressure for 4 weeks using multi-input multi-output deep neural networks. In designing the prediction model, we prepared a dataset from a clinical study. The dataset included past time-series data for blood pressure and medical examination data such as gender, age, and others. As evaluation metrics, we used the standard deviation ratio (SR) and the root-mean-square error (RMSE). Moreover, we used cross-validation as the evaluation method. RESULTS: The prediction performances of blood pressure variability and mean value after 1-4 weeks showed the SRs were "0.67" to "0.70", the RMSEs were "5.04" to "6.65" mmHg, respectively. Additionally, our models were able to work for a participant with high variability in blood pressure values due to its multi-output nature. CONCLUSION: The results of this study show that our models can predict blood pressure over 4 weeks. Our models work for an individual with high variability of blood pressure. Therefore, we consider that our prediction models are valuable for blood pressure management.
PURPOSE: The purpose of our study was to predict blood pressure variability from time-series data of blood pressure measured at home and data obtained through medical examination at a hospital. Previous studies have reported the blood pressure variability is a significant independent risk factor for cardiovascular disease. METHODS: We adopted standard deviation for a certain period and predicted variabilities and mean values of blood pressure for 4 weeks using multi-input multi-output deep neural networks. In designing the prediction model, we prepared a dataset from a clinical study. The dataset included past time-series data for blood pressure and medical examination data such as gender, age, and others. As evaluation metrics, we used the standard deviation ratio (SR) and the root-mean-square error (RMSE). Moreover, we used cross-validation as the evaluation method. RESULTS: The prediction performances of blood pressure variability and mean value after 1-4 weeks showed the SRs were "0.67" to "0.70", the RMSEs were "5.04" to "6.65" mmHg, respectively. Additionally, our models were able to work for a participant with high variability in blood pressure values due to its multi-output nature. CONCLUSION: The results of this study show that our models can predict blood pressure over 4 weeks. Our models work for an individual with high variability of blood pressure. Therefore, we consider that our prediction models are valuable for blood pressure management.